The subject matter disclosed herein relates to tomographic reconstruction, and in particular to the use of multi-dimensional dictionary learning algorithms.
Non-invasive imaging technologies allow images of the internal structures or features of a patient/object to be obtained without performing an invasive procedure on the patient/object. In particular, such non-invasive imaging technologies rely on various physical principles (such as the differential transmission of X-rays through the target volume, the reflection of acoustic waves within the volume, the paramagnetic properties of different tissues and materials within the volume, the breakdown of targeted radionuclides within the body, and so forth) to acquire data and to construct images or otherwise represent the observed internal features of the patient/object.
All reconstruction algorithms suffer from reconstruction artifacts such as streaks and noise. To reduce this artifacts, regularization based methods have been introduced. However, there are often trade-offs between computational-efficiency, dose, and image quality. Therefore, there is a need for improved reconstruction techniques, particularly in the low-dose imaging context.